Abstract
Numerous colon cancer cases are resistant to chemotherapy based on oxaliplatin and suffer from relapse. A number of survival- and prognosis-related biomarkers have been identified based on database mining for patients who develop drug resistance, but the single individual gene biomarker cannot attain high specificity and sensitivity in prognosis prediction. This work was conducted aiming to establish a new gene signature using oxaliplatin resistance-related genes to predict the prognosis for colon cancer. To this end, we downloaded gene expression profile data of cell lines that are resistant and not resistant to oxaliplatin from the Gene Expression Omnibus (GEO) database. Altogether, 495 oxaliplatin resistance-related genes were searched by weighted gene co-expression network analysis (WGCNA) and differential expression analysis. As suggested by functional analysis, the above genes were mostly enriched into cell adhesion and immune processes. Besides, a signature was built based on four oxaliplatin resistance-related genes selected from the training set to predict the overall survival (OS) by stepwise regression and least absolute shrinkage and selection operator (LASSO) Cox analysis. Relative to the low risk score group, the high risk score group had dismal OS (P < 0.0001). Moreover, the area under the curve (AUC) value regarding the 5-year OS was 0.72, indicating that the risk score was accurate in the prediction of OS for colon cancer patients (AUC >0.7). Additionally, multivariate Cox regression suggested that the signature constructed based on four oxaliplatin resistance-related genes predicted the prognosis for colon cancer cases [hazard ratio (HR), 2.77; 95% CI, 2.03–3.78; P < 0.001]. Finally, external test sets were utilized to further validate the stability and accuracy of oxaliplatin resistance-related gene signature for prognosis of colon cancer patients. To sum up, this study establishes a signature based on four oxaliplatin resistance-related genes for predicting the survival of colon cancer patients, which sheds more light on the mechanisms of oxaliplatin resistance and helps identify colon cancer cases with a dismal prognostic outcome.
Highlights
Colon cancer is a frequently occurring gastrointestinal tract (GIT) cancer
Thereafter, Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway enrichment was conducted on differentially expressed genes (DEGs) by using clusterProfiler package, which revealed that the upregulated genes were mainly enriched into the B cell receptor signaling pathway and tumor necrosis factor (TNF) signaling pathway, whereas the downregulated genes were mainly enriched into the transforming growth factor (TGF)-beta signaling pathway, Ribosome and cGMP-PKG signaling pathway (Figures 1D,E)
Colon cancer is usually featured by its intra-cancer heterogeneity; as a result, each patient is different with regard to the clinical presentations as well as treatment response [30]
Summary
Colon cancer is a frequently occurring gastrointestinal tract (GIT) cancer. As estimated by the International Agency for Research on Cancer (IARC, https://www.iarc.fr/), its morbidity and mortality rates in 2018 are 14.4 and 7.2%, separately, in the world. Colon cancer is mainly treated with surgery combined with chemotherapy [1, 2]. Great progresses have been made in the early discovery and treatment of colon cancer; as a result, its morbidity and mortality rates decrease by 30–50% among the relapsed or metastatic cases in 5 years of treatment [3]. Certain cases can gain benefits from the oxaliplatin-based chemotherapy, yet others show no treatment response because they develop resistance to oxaliplatin [6,7,8,9]. It is urgently needed to manage oxaliplatin resistance, a cause of the poor survival of patients with advanced colon cancer. It is of great significance to establish a signature based on oxaliplatin resistance- and prognosis-related genes for understanding the heterogeneities among colon cancers at the molecular level and improving treatment for these cases
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